| Citation: | Zhiheng ZHOU, Kaiyi LIU, Junchu HUANG, Zengqun CHEN. Improved Metric Learning Algorithm for Person Re-identification Based on Equidistance[J]. Journal of Electronics & Information Technology, 2019, 41(2): 477-483. doi: 10.11999/JEIT180336
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In order to improve the robustness of MLAPG algorithm, a person re-identification algorithm, called Equid-MLAPG algorithm is proposed, which is based on the equidistance measurement learning strategy. Due to the imbalanced distribution of positive and negative sample pairs in the mapping space, sample spacing hyper-parameter of MLAPG algorithm is more affected by the distance of negative sample pairs. Therefore, Equid-MLAPG algorithm tends to map the positive sample pair to be a point in the transform space. That is, the distance of a positive sample pair in the transform space is mapped to be zero, resulting in no intersection in the distribution of positive and negative sample pairs in the transform space when algorithm convergences. Experiments show that the Equid-MLAPG algorithm can achieve better experimental results on commonly used person re-identification datasets with better recognition rate and wide applicability.
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